Gravity Retaining Wall Based On Particle Swarm Algorithm To Optimize Design Research

With the further progress of China’s reform and opening up policy, the development of public infrastructure construction is booming. The gravity retaining wall is widely involved in many engineering construction projects. The conventional retaining wall’s design method requires abundant experience of designers and multiple-trails to work out the final design within all the budgets. However, it is not economical. Then some optimizations seem quite essential.In this article, particle swarm optimization algorithm, which is simple and easy to implement and better convergence, is suitable for the optimization design of gravity retaining wall. According to the economic requirements of the gravity retaining wall design, optimization design model is established. With combining the model with the particle swarm optimization algorithm, the article determines the appropriate constraints and the optimization parameters. After that, with the help of the Visual Studio2010development platform, the system development of conventional design and particle swarm optimization design of the gravity retaining wall are finished. The system mainly contains four parts, that is, the calculation of earth pressure, stability and strength calculation, particle swarm optimization design, CAD drawing. By using the system designs and calculates to engineering examples, the optimization design results showed that the optimization design effect is better when the Learning Factor C1=C2=2and the Inertia Weight w=[0.5,1]. From calculating the engineering examples by the conventional design and particle swarm optimization design. Compared to the design results, the design effect of the particle swarm optimization design is better.The contents and conclusions of this study have proved that the particle swarm optimization algorithm can be applied to the gravity retaining wall design feasibly and reliably. It has a certain practical value and provides a certain reference for the follow-up study of optimization design of gravity retaining wall.